CVE-2026-44223
6.5
Vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Exploitability: 2.8 / Impact: 3.6
Source: security-advisories@github.com (Secondary)
Description
vLLM is an inference and serving engine for large language models (LLMs). From 0.18.0 to before 0.20.0, the extract_hidden_states speculative decoding proposer in vLLM returns a tensor with an incorrect shape after the first decode step, causing a RuntimeError that crashes the EngineCore process. The crash is triggered when any request in the batch uses sampling penalty parameters (repetition_penalty, frequency_penalty, or presence_penalty). A single request with a penalty parameter (e.g., "repetition_penalty": 1.1) is sufficient to crash the server. This vulnerability is fixed in 0.20.0.
Affected (1)
Related CWEs
CWE-131
Incorrect Calculation of Buffer Size
The product does not correctly calculate the size to be used when allocating a buffer, which could lead to a buffer overflow.
CWE-704
Incorrect Type Conversion or Cast
The product does not correctly convert an object, resource, or structure from one type to a different type.
References (4)
Source: security-advisories@github.com
Issue TrackingPatch
Source: security-advisories@github.com
MitigationVendor Advisory
Source: 134c704f-9b21-4f2e-91b3-4a467353bcc0
Issue TrackingPatch
Source: 134c704f-9b21-4f2e-91b3-4a467353bcc0
MitigationVendor Advisory
Timeline
No history available yet.